Robust Machine Learning in Adversarial Setting with Provable Guarantee

Over the last decade, machine learning systems have achieved state-of-the-art performance in many fields, and are now used in increasing number of applications. However, recent research work has revealed multiple attacks to machine learning systems that significantly reduce the performance by manipulating the training or test data. As machine learning is increasingly involved in high-stake decision making processes, the robustness of machine learning systems in adversarial environment becomes a major concern. This dissertation attempts to build machine learning systems robust to such adversarial manipulation with the emphasis on providing theoretical performance guarantees. We consider adversaries in both test and training time, and make the following contributions. First, we study the robustness of machine learning algorithms and model to test-time adversarial examples. We analyze the distributional and finite sample robustness of nearest neighbor classification, and propose a modified 1-Nearest-Neighbor classifier that both has theoretical guarantee and empirical improvement in robustness. Second, we examine the robustness of malware detectors to program transformation. We propose novel attacks that evade existing detectors using program transformation, and then show program normalization as a provably robust defense against such transformation. Finally, we investigate data poisoning attacks and defenses for online learning, in which models update and predict over data stream in real-time. We show efficient attacks for general adversarial objectives, analyze the conditions for which filtering based defenses are effective, and provide practical guidance on choosing defense mechanisms and parameters.

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  • Author : Yizhen Wang
  • Publisher : Anonim
  • Pages : 178 pages
  • ISBN :
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKRobust Machine Learning in Adversarial Setting with Provable Guarantee

Robust Machine Learning in Adversarial Setting with Provable Guarantee

Robust Machine Learning in Adversarial Setting with Provable Guarantee
  • Author : Yizhen Wang
  • Publisher : Unknown Publisher
  • Release : 21 January 2021
GET THIS BOOKRobust Machine Learning in Adversarial Setting with Provable Guarantee

Over the last decade, machine learning systems have achieved state-of-the-art performance in many fields, and are now used in increasing number of applications. However, recent research work has revealed multiple attacks to machine learning systems that significantly reduce the performance by manipulating the training or test data. As machine learning is increasingly involved in high-stake decision making processes, the robustness of machine learning systems in adversarial environment becomes a major concern. This dissertation attempts to build machine learning systems robust

Adversarial Machine Learning

Adversarial Machine Learning
  • Author : Yevgeniy Vorobeychik,Murat Kantarcioglu
  • Publisher : Morgan & Claypool Publishers
  • Release : 08 August 2018
GET THIS BOOKAdversarial Machine Learning

The increasing abundance of large high-quality datasets, combined with significant technical advances over the last several decades have made machine learning into a major tool employed across a broad array of tasks including vision, language, finance, and security. However, success has been accompanied with important new challenges: many applications of machine learning are adversarial in nature. Some are adversarial because they are safety critical, such as autonomous driving. An adversary in these applications can be a malicious party aimed at

Improved Methodology for Evaluating Adversarial Robustness in Deep Neural Networks

Improved Methodology for Evaluating Adversarial Robustness in Deep Neural Networks
  • Author : Kyungmi Lee (S. M.)
  • Publisher : Unknown Publisher
  • Release : 21 January 2021
GET THIS BOOKImproved Methodology for Evaluating Adversarial Robustness in Deep Neural Networks

Deep neural networks are known to be vulnerable to adversarial perturbations, which are often imperceptible to humans but can alter predictions of machine learning systems. Since the exact value of adversarial robustness is difficult to obtain for complex deep neural networks, accuracy of the models against perturbed examples generated by attack methods is empirically used as a proxy to adversarial robustness. However, failure of attack methods to find adversarial perturbations cannot be equated with being robust. In this work, we

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
  • Author : Peggy Cellier,Kurt Driessens
  • Publisher : Springer Nature
  • Release : 27 March 2020
GET THIS BOOKMachine Learning and Knowledge Discovery in Databases

This two-volume set constitutes the refereed proceedings of the workshops which complemented the 19th Joint European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD, held in Würzburg, Germany, in September 2019. The 70 full papers and 46 short papers presented in the two-volume set were carefully reviewed and selected from 200 submissions. The two volumes (CCIS 1167 and CCIS 1168) present the papers that have been accepted for the following workshops: Workshop on Automating Data Science, ADS 2019; Workshop on Advances in Interpretable

Science of Cyber Security

Science of Cyber Security
  • Author : Feng Liu,Jia Xu,Shouhuai Xu,Moti Yung
  • Publisher : Springer Nature
  • Release : 11 January 2020
GET THIS BOOKScience of Cyber Security

This book constitutes the proceedings of the Second International Conference on Science of Cyber Security, SciSec 2019, held in Nanjing, China, in August 2019. The 20 full papers and 8 short papers presented in this volume were carefully reviewed and selected from 62 submissions. These papers cover the following subjects: Artificial Intelligence for Cybersecurity, Machine Learning for Cybersecurity, and Mechanisms for Solving Actual Cybersecurity Problems (e.g., Blockchain, Attack and Defense; Encryptions with Cybersecurity Applications).

Adversarial Risk Analysis

Adversarial Risk Analysis
  • Author : David L. Banks,Jesus M. Rios Aliaga,David Rios Insua
  • Publisher : CRC Press
  • Release : 30 June 2015
GET THIS BOOKAdversarial Risk Analysis

Winner of the 2017 De Groot Prize awarded by the International Society for Bayesian Analysis (ISBA)A relatively new area of research, adversarial risk analysis (ARA) informs decision making when there are intelligent opponents and uncertain outcomes. Adversarial Risk Analysis develops methods for allocating defensive or offensive resources against

Engineering Dependable and Secure Machine Learning Systems

Engineering Dependable and Secure Machine Learning Systems
  • Author : Onn Shehory,Eitan Farchi,Guy Barash
  • Publisher : Springer Nature
  • Release : 07 November 2020
GET THIS BOOKEngineering Dependable and Secure Machine Learning Systems

This book constitutes the revised selected papers of the Third International Workshop on Engineering Dependable and Secure Machine Learning Systems, EDSMLS 2020, held in New York City, NY, USA, in February 2020. The 7 full papers and 3 short papers were thoroughly reviewed and selected from 16 submissions. The volume presents original research on dependability and quality assurance of ML software systems, adversarial attacks on ML software systems, adversarial ML and software engineering, etc.

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

Robust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies
  • Author : National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Computer Science and Telecommunications Board,Board on Mathematical Sciences and Analytics,Intelligence Community Studies Board
  • Publisher : National Academies Press
  • Release : 22 August 2019
GET THIS BOOKRobust Machine Learning Algorithms and Systems for Detection and Mitigation of Adversarial Attacks and Anomalies

The Intelligence Community Studies Board (ICSB) of the National Academies of Sciences, Engineering, and Medicine convened a workshop on December 11â€"12, 2018, in Berkeley, California, to discuss robust machine learning algorithms and systems for the detection and mitigation of adversarial attacks and anomalies. This publication summarizes the presentations and discussions from the workshop.

Strengthening Deep Neural Networks

Strengthening Deep Neural Networks
  • Author : Katy Warr
  • Publisher : O'Reilly Media
  • Release : 03 July 2019
GET THIS BOOKStrengthening Deep Neural Networks

As deep neural networks (DNNs) become increasingly common in real-world applications, the potential to deliberately "fool" them with data that wouldn’t trick a human presents a new attack vector. This practical book examines real-world scenarios where DNNs—the algorithms intrinsic to much of AI—are used daily to process image, audio, and video data. Author Katy Warr considers attack motivations, the risks posed by this adversarial input, and methods for increasing AI robustness to these attacks. If you’re

Cross-Lingual Word Embeddings

Cross-Lingual Word Embeddings
  • Author : Anders Søgaard,Ivan Vulić,Sebastian Ruder,Manaal Faruqui
  • Publisher : Morgan & Claypool Publishers
  • Release : 04 June 2019
GET THIS BOOKCross-Lingual Word Embeddings

The majority of natural language processing (NLP) is English language processing, and while there is good language technology support for (standard varieties of) English, support for Albanian, Burmese, or Cebuano—and most other languages—remains limited. Being able to bridge this digital divide is important for scientific and democratic reasons but also represents an enormous growth potential. A key challenge for this to happen is learning to align basic meaning-bearing units of different languages. In this book, the authors survey

Advances in Artificial Intelligence

Advances in Artificial Intelligence
  • Author : Cyril Goutte,Xiaodan Zhu
  • Publisher : Springer Nature
  • Release : 05 May 2020
GET THIS BOOKAdvances in Artificial Intelligence

This book constitutes the refereed proceedings of the 33rd Canadian Conference on Artificial Intelligence, Canadian AI 2020, which was planned to take place in Ottawa, ON, Canada. Due to the COVID-19 pandemic, however, it was held virtually during May 13–15, 2020. The 31 regular papers and 24 short papers presented together with 4 Graduate Student Symposium papers were carefully reviewed and selected from a total of 175 submissions. The selected papers cover a wide range of topics, including machine learning, pattern recognition, natural language processing, knowledge representation,

Artificial Intelligence Safety and Security

Artificial Intelligence Safety and Security
  • Author : Roman V. Yampolskiy
  • Publisher : CRC Press
  • Release : 27 July 2018
GET THIS BOOKArtificial Intelligence Safety and Security

The history of robotics and artificial intelligence in many ways is also the history of humanity’s attempts to control such technologies. From the Golem of Prague to the military robots of modernity, the debate continues as to what degree of independence such entities should have and how to make sure that they do not turn on us, its inventors. Numerous recent advancements in all aspects of research, development and deployment of intelligent systems are well publicized but safety and

Towards Robust Deep Neural Networks

Towards Robust Deep Neural Networks
  • Author : Andras Rozsa
  • Publisher : Unknown Publisher
  • Release : 21 January 2021
GET THIS BOOKTowards Robust Deep Neural Networks

One of the greatest technological advancements of the 21st century has been the rise of machine learning. This thriving field of research already has a great impact on our lives and, considering research topics and the latest advancements, will continue to rapidly grow. In the last few years, the most powerful machine learning models have managed to reach or even surpass human level performance on various challenging tasks, including object or face recognition in photographs. Although we are capable of